First the libraries to be use will be imported

# importing neccessary libraries 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Dataset to be use (carPrice_data) need to be loaded as done below

# loading the dataset to be worked on
cp = pd.read_csv('CarPrice_data.csv')
cp

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0
1	2	3	alfa-romero stelvio	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	16500.0
2	3	1	alfa-romero Quadrifoglio	gas	std	two	hatchback	rwd	front	94.5	...	152	mpfi	2.68	3.47	9.0	154	5000	19	26	16500.0
3	4	2	audi 100 ls	gas	std	four	sedan	fwd	front	99.8	...	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950.0
4	5	2	audi 100ls	gas	std	four	sedan	4wd	front	99.4	...	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
200	201	-1	volvo 145e (sw)	gas	std	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	23	28	16845.0
201	202	-1	volvo 144ea	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	8.7	160	5300	19	25	19045.0
202	203	-1	volvo 244dl	gas	std	four	sedan	rwd	front	109.1	...	173	mpfi	3.58	2.87	8.8	134	5500	18	23	21485.0
203	204	-1	volvo 246	diesel	turbo	four	sedan	rwd	front	109.1	...	145	idi	3.01	3.40	23.0	106	4800	26	27	22470.0
204	205	-1	volvo 264gl	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	19	25	22625.0

205 rows × 26 columns

ACCESSING THE DATASET

# checking the summary of the data set using the dot describe method
cp.describe()

	car_ID	symboling	wheelbase	carlength	carwidth	carheight	curbweight	enginesize	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
count	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000	205.000000
mean	103.000000	0.834146	98.756585	174.049268	65.907805	53.724878	2555.565854	126.907317	3.329756	3.255415	10.142537	104.117073	5125.121951	25.219512	30.751220	13276.710571
std	59.322565	1.245307	6.021776	12.337289	2.145204	2.443522	520.680204	41.642693	0.270844	0.313597	3.972040	39.544167	476.985643	6.542142	6.886443	7988.852332
min	1.000000	-2.000000	86.600000	141.100000	60.300000	47.800000	1488.000000	61.000000	2.540000	2.070000	7.000000	48.000000	4150.000000	13.000000	16.000000	5118.000000
25%	52.000000	0.000000	94.500000	166.300000	64.100000	52.000000	2145.000000	97.000000	3.150000	3.110000	8.600000	70.000000	4800.000000	19.000000	25.000000	7788.000000
50%	103.000000	1.000000	97.000000	173.200000	65.500000	54.100000	2414.000000	120.000000	3.310000	3.290000	9.000000	95.000000	5200.000000	24.000000	30.000000	10295.000000
75%	154.000000	2.000000	102.400000	183.100000	66.900000	55.500000	2935.000000	141.000000	3.580000	3.410000	9.400000	116.000000	5500.000000	30.000000	34.000000	16503.000000
max	205.000000	3.000000	120.900000	208.100000	72.300000	59.800000	4066.000000	326.000000	3.940000	4.170000	23.000000	288.000000	6600.000000	49.000000	54.000000	45400.000000

# checking the total null values in the dataset
cp.isnull().sum()

car_ID              0
symboling           0
CarName             0
fueltype            0
aspiration          0
doornumber          0
carbody             0
drivewheel          0
enginelocation      0
wheelbase           0
carlength           0
carwidth            0
carheight           0
curbweight          0
enginetype          0
cylindernumber      0
enginesize          0
fuelsystem          0
boreratio           0
stroke              0
compressionratio    0
horsepower          0
peakrpm             0
citympg             0
highwaympg          0
price               0
dtype: int64

cp.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 205 entries, 0 to 204
Data columns (total 26 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   car_ID            205 non-null    int64  
 1   symboling         205 non-null    int64  
 2   CarName           205 non-null    object 
 3   fueltype          205 non-null    object 
 4   aspiration        205 non-null    object 
 5   doornumber        205 non-null    object 
 6   carbody           205 non-null    object 
 7   drivewheel        205 non-null    object 
 8   enginelocation    205 non-null    object 
 9   wheelbase         205 non-null    float64
 10  carlength         205 non-null    float64
 11  carwidth          205 non-null    float64
 12  carheight         205 non-null    float64
 13  curbweight        205 non-null    int64  
 14  enginetype        205 non-null    object 
 15  cylindernumber    205 non-null    object 
 16  enginesize        205 non-null    int64  
 17  fuelsystem        205 non-null    object 
 18  boreratio         205 non-null    float64
 19  stroke            205 non-null    float64
 20  compressionratio  205 non-null    float64
 21  horsepower        205 non-null    int64  
 22  peakrpm           205 non-null    int64  
 23  citympg           205 non-null    int64  
 24  highwaympg        205 non-null    int64  
 25  price             205 non-null    float64
dtypes: float64(8), int64(8), object(10)
memory usage: 41.8+ KB

DATA PROCESSING

# checking the total null values in the dataset
cp.isnull().sum()

car_ID              0
symboling           0
CarName             0
fueltype            0
aspiration          0
doornumber          0
carbody             0
drivewheel          0
enginelocation      0
wheelbase           0
carlength           0
carwidth            0
carheight           0
curbweight          0
enginetype          0
cylindernumber      0
enginesize          0
fuelsystem          0
boreratio           0
stroke              0
compressionratio    0
horsepower          0
peakrpm             0
citympg             0
highwaympg          0
price               0
dtype: int64

from the result of the isnull sum above, it shows that the dataset is complete with no missing values as all columns shows zero as the sum of their isnull hence no need for any further processing

CARRYING OUT EXPLORATORY DATA ANALYSIS

First Pandas profiling will be carried out on the dataset to see the descriptive summary of the data

# impporting pandas profiling library
from pandas_profiling import ProfileReport

profile = ProfileReport(cp)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]

From the result obtained above, the car price dataset is summarize to have 26 Number of variable out of which 16 are numerical while 10 are categorical variables and 205 Number of observations

univariate analysis:

cp.head()

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0
1	2	3	alfa-romero stelvio	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	16500.0
2	3	1	alfa-romero Quadrifoglio	gas	std	two	hatchback	rwd	front	94.5	...	152	mpfi	2.68	3.47	9.0	154	5000	19	26	16500.0
3	4	2	audi 100 ls	gas	std	four	sedan	fwd	front	99.8	...	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950.0
4	5	2	audi 100ls	gas	std	four	sedan	4wd	front	99.4	...	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450.0

5 rows × 26 columns

# plot to show the most demanded cars in the given data set for the first 50 rows
sns.catplot(data=cp.head(50), y='CarName', kind='count').set(title='TOP DEMANDED CAR NAMES')

<seaborn.axisgrid.FacetGrid at 0x1ca48491d30>

The result above shows that honda civic (2 times more) with audi 100ls, bmw 320i, bmw x3, honda civic cvcc, honda accord and isuzu D-max

# last 55 columns
sns.catplot(data=cp.tail(55), y='CarName', kind='count').set(title='TOP DEMANDED CAR NAMES')

<seaborn.axisgrid.FacetGrid at 0x1ca4215ba00>

here, the most sorted after cars include toyota corona and toyota corolla (6 times more), follow by toyota mark ii (3 times more than other cars)

price = cp[['CarName', 'price']]
price.head(30)

	CarName	price
0	alfa-romero giulia	13495.000
1	alfa-romero stelvio	16500.000
2	alfa-romero Quadrifoglio	16500.000
3	audi 100 ls	13950.000
4	audi 100ls	17450.000
5	audi fox	15250.000
6	audi 100ls	17710.000
7	audi 5000	18920.000
8	audi 4000	23875.000
9	audi 5000s (diesel)	17859.167
10	bmw 320i	16430.000
11	bmw 320i	16925.000
12	bmw x1	20970.000
13	bmw x3	21105.000
14	bmw z4	24565.000
15	bmw x4	30760.000
16	bmw x5	41315.000
17	bmw x3	36880.000
18	chevrolet impala	5151.000
19	chevrolet monte carlo	6295.000
20	chevrolet vega 2300	6575.000
21	dodge rampage	5572.000
22	dodge challenger se	6377.000
23	dodge d200	7957.000
24	dodge monaco (sw)	6229.000
25	dodge colt hardtop	6692.000
26	dodge colt (sw)	7609.000
27	dodge coronet custom	8558.000
28	dodge dart custom	8921.000
29	dodge coronet custom (sw)	12964.000

sns.catplot(data=price.tail(55), y='CarName',x='price')

<seaborn.axisgrid.FacetGrid at 0x1ca5bb81400>

GENERALLY, the bmw models are the are that are with the highest prices, follow by audi cars. The result of the cars with their prices above shows that bmw x5 is the car with the highest price, follow by bmw x3 then bmw x4

# To know the most sorted after fuel type
cp['fueltype'].hist(color= 'white', edgecolor= 'green')

<AxesSubplot:>

From the above result, the most sorted after fuel type is gas whivh has over 90 percent of the customers going for it with diesel fuel type having less than 25 percent of the total sortings

# To know the aspirations of customers
cp['aspiration'].hist(color = 'green', edgecolor = 'red')

<AxesSubplot:>

observation: This shows that the standard (std) car type is highly prefarred(over five times) compared to the turbo

# most preferred number of doors in a car 
cp['doornumber'].hist(color='yellow', edgecolor = 'black')

<AxesSubplot:>

obsevation: though cars with four doors are preferred more than those with two doors, there is virtually no significamt differnce among custon=mers preference regarding numbwer of car doors as seen in the visuals above

# car body
cp['carbody'].hist(edgecolor='yellow')

<AxesSubplot:>

It is observed from the result above that the sedan car type interest more of the customers follow by hatchback then the wagon type while the convertible and hardtop are less preferred

# drivewheel
cp['drivewheel'].hist(color='white', edgecolor= 'blue')

<AxesSubplot:>

this shows that the forewheeldrive attracts higher customers (100 percent) than the rear wheel drive car (75%), fourwheel drive cars are very less sorted after by this customer base (less than 15%)

# location of engine 
cp['enginelocation'].hist(color='orange', edgecolor='green')

<AxesSubplot:>

as shown in the visuals above, almost all customers if not all go for the front engine location as against the one with engines in the rear. less than 2% sort ofter the rear engine base

cp['wheelbase'].hist(edgecolor='white')

<AxesSubplot:>

wheelbase btween 95 to 105 is most sorted after

cp['enginesize'].hist(edgecolor='red')

<AxesSubplot:>

preference to engine size decrese with an increase in the size of the engine within the cusotomer base of this analysis

cp['fuelsystem'].hist(color='white', edgecolor='orange')

<AxesSubplot:>

cp['boreratio'].hist(edgecolor='orange')

<AxesSubplot:>

cp['stroke'].hist(edgecolor='white')

<AxesSubplot:>

cp['horsepower'].hist(color='green', edgecolor='blue')

<AxesSubplot:>

cp['peakrpm'].hist(color='yellow', edgecolor='black')

<AxesSubplot:>

most purchase cars rgarding rpm are those within 4500 and 6000 with 5500rpm as the most preferred

cp['citympg'].hist(color='black', edgecolor='white')

<AxesSubplot:>

cp['highwaympg'].hist(edgecolor='black')

<AxesSubplot:>

cp['price'].hist(color='white', edgecolor='black')

<AxesSubplot:>

observation: The lower the price the higher the purchase and vice versa

BIVARIATE ANALYSIS

comparing a car feature to another from the above dataset

cp.head(1)

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0

1 rows × 26 columns

#Top cars with the highest price
hprice=cp[['CarName', 'price']].head(8).groupby('CarName').sum()
hprice

	price
CarName
alfa-romero Quadrifoglio	16500.0
alfa-romero giulia	13495.0
alfa-romero stelvio	16500.0
audi 100 ls	13950.0
audi 100ls	35160.0
audi 5000	18920.0
audi fox	15250.0

#visualization of the above extract
plt.style.use('seaborn')
hprice.plot(figsize=(13,4), color='green', legend=False, title= 'TOP CARS WITH HIGHEST PRICE')

<AxesSubplot:title={'center':'TOP CARS WITH HIGHEST PRICE'}, xlabel='CarName'>

cp.head(1)

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0

1 rows × 26 columns

# univariate analysis beween door number and car body

sns.catplot(data=cp, x='doornumber', y= 'carbody')

<seaborn.axisgrid.FacetGrid at 0x1ca541c0610>

Observation: is observed from the result above that cars with wagon body purcase by the customerbase are generally four door cars, hardtop carbody are two door cars occassionally purchase by the customer base, sedan are two and four door cars though four doors is more consistent with this carbody type, hatchback; two and four with two more consistently preferred by the customers, while convertible carbody are generally two doors with purchase not consistent.

# fueltype with engine size
sns.catplot(data=cp, x='fueltype', y= 'price')

<seaborn.axisgrid.FacetGrid at 0x1ca5af5db50>

observation: cars with gas fuel type are generally more preferred. from the result above, cars with gas engines and less engine size are mostly preferred with less preference as the engine engine size increse. diesel engines with engine size less than 200 are spercely purchsaed.

sns.catplot(data=cp, y='enginesize', x= 'enginelocation')

<seaborn.axisgrid.FacetGrid at 0x1ca5b04ff40>

sns.catplot(data=cp, x='aspiration', y= 'price')

<seaborn.axisgrid.FacetGrid at 0x1ca3e5e68e0>

as the horse power increase for a car, the prefernce for that car decrease for a front engine car. rear engine locations are not even to be mention as thet are very very rarely preferred

sns.catplot(data=cp, x='carbody', y= 'price')

<seaborn.axisgrid.FacetGrid at 0x1ca53f0b700>

cars with sedan bodies are consistently preferred even with increase in price.

sns.catplot(data=cp, x='aspiration', y= 'price')

<seaborn.axisgrid.FacetGrid at 0x1ca540f4d60>

sns.jointplot(data=cp, x='horsepower',y='price',kind='hex')

<seaborn.axisgrid.JointGrid at 0x1ca54165490>

MULTIVARIATE ANALYSIS

cp.head(1)

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0

1 rows × 26 columns

cshape=cp[['carlength', 'carwidth','carheight','curbweight' ]].head(8).groupby('curbweight').sum()
cshape

	carlength	carwidth	carheight
curbweight
2337	176.6	66.2	54.3
2507	177.3	66.3	53.1
2548	337.6	128.2	97.6
2823	171.2	65.5	52.4
2824	176.6	66.4	54.3
2844	192.7	71.4	55.7
2954	192.7	71.4	55.7

cshape.plot(figsize=(13,4),  title= 'CAR MEASUREMENTS PREFERENCE')

<AxesSubplot:title={'center':'CAR MEASUREMENTS PREFERENCE'}, xlabel='curbweight'>

sns.pairplot(cp[['carlength','carwidth','carheight','curbweight']])

<seaborn.axisgrid.PairGrid at 0x1ca5434aaf0>

sns.pairplot(cp)

<seaborn.axisgrid.PairGrid at 0x1ca4fc08430>

sns.heatmap(cp.corr(), annot=True)

<AxesSubplot:>

cp.corr()

	car_ID	symboling	wheelbase	carlength	carwidth	carheight	curbweight	enginesize	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
car_ID	1.000000	-0.151621	0.129729	0.170636	0.052387	0.255960	0.071962	-0.033930	0.260064	-0.160824	0.150276	-0.015006	-0.203789	0.015940	0.011255	-0.109093
symboling	-0.151621	1.000000	-0.531954	-0.357612	-0.232919	-0.541038	-0.227691	-0.105790	-0.130051	-0.008735	-0.178515	0.070873	0.273606	-0.035823	0.034606	-0.079978
wheelbase	0.129729	-0.531954	1.000000	0.874587	0.795144	0.589435	0.776386	0.569329	0.488750	0.160959	0.249786	0.353294	-0.360469	-0.470414	-0.544082	0.577816
carlength	0.170636	-0.357612	0.874587	1.000000	0.841118	0.491029	0.877728	0.683360	0.606454	0.129533	0.158414	0.552623	-0.287242	-0.670909	-0.704662	0.682920
carwidth	0.052387	-0.232919	0.795144	0.841118	1.000000	0.279210	0.867032	0.735433	0.559150	0.182942	0.181129	0.640732	-0.220012	-0.642704	-0.677218	0.759325
carheight	0.255960	-0.541038	0.589435	0.491029	0.279210	1.000000	0.295572	0.067149	0.171071	-0.055307	0.261214	-0.108802	-0.320411	-0.048640	-0.107358	0.119336
curbweight	0.071962	-0.227691	0.776386	0.877728	0.867032	0.295572	1.000000	0.850594	0.648480	0.168790	0.151362	0.750739	-0.266243	-0.757414	-0.797465	0.835305
enginesize	-0.033930	-0.105790	0.569329	0.683360	0.735433	0.067149	0.850594	1.000000	0.583774	0.203129	0.028971	0.809769	-0.244660	-0.653658	-0.677470	0.874145
boreratio	0.260064	-0.130051	0.488750	0.606454	0.559150	0.171071	0.648480	0.583774	1.000000	-0.055909	0.005197	0.573677	-0.254976	-0.584532	-0.587012	0.553173
stroke	-0.160824	-0.008735	0.160959	0.129533	0.182942	-0.055307	0.168790	0.203129	-0.055909	1.000000	0.186110	0.080940	-0.067964	-0.042145	-0.043931	0.079443
compressionratio	0.150276	-0.178515	0.249786	0.158414	0.181129	0.261214	0.151362	0.028971	0.005197	0.186110	1.000000	-0.204326	-0.435741	0.324701	0.265201	0.067984
horsepower	-0.015006	0.070873	0.353294	0.552623	0.640732	-0.108802	0.750739	0.809769	0.573677	0.080940	-0.204326	1.000000	0.131073	-0.801456	-0.770544	0.808139
peakrpm	-0.203789	0.273606	-0.360469	-0.287242	-0.220012	-0.320411	-0.266243	-0.244660	-0.254976	-0.067964	-0.435741	0.131073	1.000000	-0.113544	-0.054275	-0.085267
citympg	0.015940	-0.035823	-0.470414	-0.670909	-0.642704	-0.048640	-0.757414	-0.653658	-0.584532	-0.042145	0.324701	-0.801456	-0.113544	1.000000	0.971337	-0.685751
highwaympg	0.011255	0.034606	-0.544082	-0.704662	-0.677218	-0.107358	-0.797465	-0.677470	-0.587012	-0.043931	0.265201	-0.770544	-0.054275	0.971337	1.000000	-0.697599
price	-0.109093	-0.079978	0.577816	0.682920	0.759325	0.119336	0.835305	0.874145	0.553173	0.079443	0.067984	0.808139	-0.085267	-0.685751	-0.697599	1.000000

by this we now know the numerical columns hence the need to drop the categorical

cp.columns

Index(['car_ID', 'symboling', 'CarName', 'fueltype', 'aspiration',
       'doornumber', 'carbody', 'drivewheel', 'enginelocation', 'wheelbase',
       'carlength', 'carwidth', 'carheight', 'curbweight', 'enginetype',
       'cylindernumber', 'enginesize', 'fuelsystem', 'boreratio', 'stroke',
       'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg',
       'price'],
      dtype='object')

PREPARING DATA FOR MODELLING

cp.info() 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 205 entries, 0 to 204
Data columns (total 26 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   car_ID            205 non-null    int64  
 1   symboling         205 non-null    int64  
 2   CarName           205 non-null    object 
 3   fueltype          205 non-null    object 
 4   aspiration        205 non-null    object 
 5   doornumber        205 non-null    object 
 6   carbody           205 non-null    object 
 7   drivewheel        205 non-null    object 
 8   enginelocation    205 non-null    object 
 9   wheelbase         205 non-null    float64
 10  carlength         205 non-null    float64
 11  carwidth          205 non-null    float64
 12  carheight         205 non-null    float64
 13  curbweight        205 non-null    int64  
 14  enginetype        205 non-null    object 
 15  cylindernumber    205 non-null    object 
 16  enginesize        205 non-null    int64  
 17  fuelsystem        205 non-null    object 
 18  boreratio         205 non-null    float64
 19  stroke            205 non-null    float64
 20  compressionratio  205 non-null    float64
 21  horsepower        205 non-null    int64  
 22  peakrpm           205 non-null    int64  
 23  citympg           205 non-null    int64  
 24  highwaympg        205 non-null    int64  
 25  price             205 non-null    float64
dtypes: float64(8), int64(8), object(10)
memory usage: 41.8+ KB

cp.duplicated().sum()

0

The result above shows that there is no null or duplicated values anywhere on our data frame

# target variable
y = cp['price']
x = cp
x

	car_ID	symboling	CarName	fueltype	aspiration	doornumber	carbody	drivewheel	enginelocation	wheelbase	...	enginesize	fuelsystem	boreratio	stroke	compressionratio	horsepower	peakrpm	citympg	highwaympg	price
0	1	3	alfa-romero giulia	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	13495.0
1	2	3	alfa-romero stelvio	gas	std	two	convertible	rwd	front	88.6	...	130	mpfi	3.47	2.68	9.0	111	5000	21	27	16500.0
2	3	1	alfa-romero Quadrifoglio	gas	std	two	hatchback	rwd	front	94.5	...	152	mpfi	2.68	3.47	9.0	154	5000	19	26	16500.0
3	4	2	audi 100 ls	gas	std	four	sedan	fwd	front	99.8	...	109	mpfi	3.19	3.40	10.0	102	5500	24	30	13950.0
4	5	2	audi 100ls	gas	std	four	sedan	4wd	front	99.4	...	136	mpfi	3.19	3.40	8.0	115	5500	18	22	17450.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
200	201	-1	volvo 145e (sw)	gas	std	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	23	28	16845.0
201	202	-1	volvo 144ea	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	8.7	160	5300	19	25	19045.0
202	203	-1	volvo 244dl	gas	std	four	sedan	rwd	front	109.1	...	173	mpfi	3.58	2.87	8.8	134	5500	18	23	21485.0
203	204	-1	volvo 246	diesel	turbo	four	sedan	rwd	front	109.1	...	145	idi	3.01	3.40	23.0	106	4800	26	27	22470.0
204	205	-1	volvo 264gl	gas	turbo	four	sedan	rwd	front	109.1	...	141	mpfi	3.78	3.15	9.5	114	5400	19	25	22625.0

205 rows × 26 columns

# encode categorical features to numerical ones
x =  pd.get_dummies(x)
x.head()

	car_ID	symboling	wheelbase	carlength	carwidth	carheight	curbweight	enginesize	boreratio	stroke	...	cylindernumber_twelve	cylindernumber_two	fuelsystem_1bbl	fuelsystem_2bbl	fuelsystem_4bbl	fuelsystem_idi	fuelsystem_mfi	fuelsystem_mpfi	fuelsystem_spdi	fuelsystem_spfi
0	1	3	88.6	168.8	64.1	48.8	2548	130	3.47	2.68	...	0	0	0	0	0	0	0	1	0	0
1	2	3	88.6	168.8	64.1	48.8	2548	130	3.47	2.68	...	0	0	0	0	0	0	0	1	0	0
2	3	1	94.5	171.2	65.5	52.4	2823	152	2.68	3.47	...	0	0	0	0	0	0	0	1	0	0
3	4	2	99.8	176.6	66.2	54.3	2337	109	3.19	3.40	...	0	0	0	0	0	0	0	1	0	0
4	5	2	99.4	176.6	66.4	54.3	2824	136	3.19	3.40	...	0	0	0	0	0	0	0	1	0	0

5 rows × 201 columns

SCALING THE DATASET SO THE VALUES ARE REDUCED TO THE LOWEST POSSIBLE FOR EASE OF PREDICTION BY THE MACHINE (BELOW)

#IMPORTING SCALING LIBRARY
from sklearn.preprocessing import StandardScaler   

#scaling the dataset
scaler = StandardScaler()
x = scaler.fit_transform(x)
x

array([[-1.72362229,  1.74347043, -1.6907718 , ...,  1.08666971,
        -0.21428571, -0.070014  ],
       [-1.70672403,  1.74347043, -1.6907718 , ...,  1.08666971,
        -0.21428571, -0.070014  ],
       [-1.68982577,  0.133509  , -0.70859588, ...,  1.08666971,
        -0.21428571, -0.070014  ],
       ...,
       [ 1.68982577, -1.47645244,  1.72187336, ...,  1.08666971,
        -0.21428571, -0.070014  ],
       [ 1.70672403, -1.47645244,  1.72187336, ..., -0.92024282,
        -0.21428571, -0.070014  ],
       [ 1.72362229, -1.47645244,  1.72187336, ...,  1.08666971,
        -0.21428571, -0.070014  ]])

The above shows a convertedly reduced array of result for the data set

SPLITTING THE DATASET FOR PREDICTION (BELOW)

#importing the train_test_split library
from sklearn.model_selection import train_test_split

#splitting the dataset for trainig and testing the dataset
x_train, x_test, y_train, y_test= train_test_split(x,y, test_size=0.3, random_state=65)

BUILDING A REGRESSION MODEL

#importing library
from sklearn.linear_model import LinearRegression

modella = LinearRegression()

modella.fit(x_train, y_train) 

LinearRegression()

print(modella.intercept_)

13264.22306364097

print(modella.coef_)

[-7.57154339e-11 -4.54747351e-13  8.98126018e-12 -2.54658516e-11
  9.60653779e-12 -1.66551217e-11  5.05906428e-11 -1.12947873e-10
 -1.00115471e-11  4.87787588e-12 -9.32232069e-12  9.77706804e-11
 -2.60911293e-11  5.37738742e-11 -4.91695573e-11  7.96934351e+03
 -8.08004661e+00 -9.94759515e+00 -3.63206093e+00 -3.63206093e+00
 -6.33098678e+00 -7.88641428e+00 -5.59025546e+00 -5.59025546e+00
 -1.23091194e+01 -5.59025546e+00 -1.13988700e+01 -1.34963735e+01
 -1.13988700e+01 -8.08004661e+00 -8.08004661e+00 -8.08004661e+00
 -5.59025546e+00 -1.23901843e+01 -1.16035783e+01 -1.04329040e+01
 -1.39656187e+01 -5.41865844e+00 -1.02305074e+01 -9.51763299e+00
 -7.49577645e+00 -8.08004661e+00 -1.17767203e+01 -1.73171335e+01
 -8.08004661e+00 -8.08004661e+00 -8.08004661e+00 -7.64485549e+00
 -8.08004661e+00 -8.08004661e+00 -4.78554209e+00 -1.76619361e+01
 -1.13988700e+01 -8.08004661e+00 -8.08004661e+00 -1.39262791e+01
 -8.08004661e+00 -8.08004661e+00 -8.08004661e+00 -4.02073021e+01
 -8.08004661e+00 -1.13988700e+01 -1.09776586e+01 -8.08004661e+00
 -8.08004661e+00 -8.08004661e+00 -6.67185052e+00 -8.08004661e+00
 -8.08004661e+00 -1.39262791e+01 -4.48842865e+01 -7.39245329e+00
 -1.36322539e+01 -8.08004661e+00 -1.13988700e+01 -1.13988700e+01
 -1.13988700e+01 -9.11188066e+00 -8.15803754e+00 -1.39262791e+01
 -8.08004661e+00 -8.08004661e+00 -1.39262791e+01 -1.32227502e+01
 -1.39262791e+01 -8.08004661e+00 -1.13988700e+01 -8.08004661e+00
 -8.08004661e+00 -1.13607395e+01 -8.08004661e+00 -1.54009285e+01
 -1.13988700e+01 -8.08004661e+00 -8.08004661e+00 -1.51152473e+01
 -9.11723453e+00 -1.13988700e+01 -8.08004661e+00 -8.08004661e+00
 -1.28212579e+01 -2.66434290e+01 -1.28771287e+01 -1.10129341e+01
 -1.55364207e+01 -8.08004661e+00 -1.13445679e+01 -1.56083094e+01
 -1.13988700e+01 -8.08004661e+00 -8.08004661e+00 -1.96538218e+01
 -1.26945559e+01  1.67970861e+01 -1.26945559e+01 -8.08004661e+00
 -8.08004661e+00 -1.13988700e+01 -1.13988700e+01 -1.13988700e+01
 -9.32004206e+00 -6.60647718e+00 -6.60647718e+00 -1.31154404e+01
 -6.60647718e+00 -6.60647718e+00 -6.60647718e+00 -6.60647718e+00
 -1.26945559e+01 -1.26945559e+01 -8.08004661e+00 -1.95479377e+01
 -1.13988700e+01 -8.08004661e+00 -1.13988700e+01 -8.08004661e+00
 -1.95479377e+01 -1.26945559e+01 -8.08004661e+00 -8.08004661e+00
 -3.63206093e+00 -1.39262791e+01 -1.13988700e+01 -8.08004661e+00
 -8.08004661e+00 -8.08004661e+00 -1.26945559e+01 -1.26945559e+01
 -1.13988700e+01 -8.08004661e+00 -5.59025546e+00 -1.26945559e+01
 -1.26945559e+01 -8.08004661e+00 -1.13988700e+01 -1.13988700e+01
 -1.13988700e+01 -8.08004661e+00 -1.05891092e+01 -1.13988700e+01
 -8.08004661e+00 -3.63206093e+00 -8.08004661e+00 -2.40842956e+01
 -3.61477868e+01  1.58237867e-11 -1.58237867e-11  2.82526510e+00
  2.82526510e+00 -1.24061423e+01 -1.89013840e+00 -4.62840552e+00
 -4.87039701e+00 -3.19390324e+00  5.74493428e+00  1.38145219e+01
  1.35437031e+01 -1.81505699e+01  4.33704334e+00  1.15008482e+00
 -2.55308398e+01  1.10321927e+01  2.19497148e+00 -4.23199606e+00
  1.19394105e+00 -3.01833272e+00  7.06512616e+00 -1.67676614e+01
 -1.61340160e+01 -1.24339729e+01 -6.21166941e+00 -4.10270789e+00
 -3.01833272e+00 -9.12914017e+00 -1.89283185e+01 -3.67975047e+00
 -2.40842956e+01 -3.25778081e+00 -2.01863632e+01 -8.30007388e+00
 -2.82258969e+00]

EVALUATING MODEL

prediction = modella.predict(x_test)
prediction

array([ 9279.        , 11504.32006909,  6561.90361144, 18344.        ,
       20892.26015994,  5434.47237812,  8874.91094984,  7738.        ,
       21951.76854448, 10515.30988615, 16501.88060626, 17950.        ,
       34480.9819704 ,  7051.91263846,  8428.76854448, 14869.        ,
        6835.90361144,  7395.        ,  6276.28559793,  9639.        ,
        6851.76854448, 31533.39906289, 13174.04536176,  7908.76854448,
        8711.76854448,  6521.94212051,  9448.02467361, 19593.92432726,
       18420.        , 28150.40149997,  7198.        , 13778.76854448,
       16473.19531702, 10631.76854448,  6649.        ,  6244.42128516,
       25482.49409077, 13484.11337569,  6780.1902191 , 40993.9020609 ,
       12413.24345475, 36880.        , 37064.04408145, 45522.67341637,
        9428.76854448, 10898.        , 22433.98774906,  7603.        ,
       17939.32006909,  8238.        , 13975.10402981, 17762.73201347,
        7126.        , 11900.        , 16900.        , 15510.        ,
       12717.14474808, 11549.        ,  7500.94781582, 16925.        ,
        6849.        ,  6915.18743895])

y_test

88      9279.0
56     11845.0
31      6855.0
66     18344.0
12     20970.0
        ...   
171    11549.0
122     7609.0
11     16925.0
92      6849.0
85      6989.0
Name: price, Length: 62, dtype: float64

## comparing y_test to the prediction to find residuals
plt.scatter(y_test, prediction/10**6)

<matplotlib.collections.PathCollection at 0x1ca3b998a60>

import warnings
warnings.filterwarnings('ignore')

sns.distplot(y_test- prediction/10**6)

<AxesSubplot:xlabel='price', ylabel='Density'>

EVALUATING MODEL PERFORMANCE

# importing evaluation library
from sklearn import metrics

# average error of the model
MAE= metrics.mean_absolute_error(y_test, prediction)
MAE

64.45034302127931

# checking larger errors by squaring 
MSE = metrics.mean_squared_error(y_test, prediction)
MSE

12785.927056214425

# checking for sqMSE 
np.sqrt(metrics.mean_squared_error(y_test, prediction))

113.07487367321896

r_squared=modella.score(x,y)
print(r_squared)

0.999939112946569

r_squared = modella.score(x_test, prediction)
print(r_squared)

1.0

from sklearn.metrics import r2_score

r2_score(y_test, prediction)

0.9998509888729505

#importing dataset to test the perfomance of the model 

from sklearn.svm import SVC #so that the model will know you want to the test a new data set

modella.fit(x,y)

LinearRegression()

y_pred = modella.predict(x_test)
y_pred

array([ 9279.   , 11845.   ,  6855.   , 18344.   , 20970.   ,  5572.   ,
        8916.5  ,  7738.   , 22018.   , 10595.   , 16503.   , 17950.   ,
       34028.   ,  7099.   ,  8495.   , 14869.   ,  7129.   ,  7395.   ,
        6229.   ,  9639.   ,  6918.   , 31600.   , 13200.   ,  7975.   ,
        8778.   ,  6575.   ,  9549.   , 19699.   , 18420.   , 28248.   ,
        7198.   , 13845.   , 16500.   , 10698.   ,  6649.   ,  6377.   ,
       25552.   , 13499.   ,  6795.   , 40960.   , 12440.   , 36880.   ,
       37028.   , 45400.   ,  9495.   , 10898.   , 22470.   ,  7603.   ,
       18280.   ,  8238.   , 13950.   , 17859.167,  7126.   , 11900.   ,
       16900.   , 15510.   , 12764.   , 11549.   ,  7609.   , 16925.   ,
        6849.   ,  6989.   ])

# evaluating accuracy 
from sklearn.metrics import  accuracy_score
accuracy = modella.score(x_test, y_pred)

accuracy

1.0

INSIGHTS/OBSERVATIONS AND RECOMMENDATIONS

From the analysis carried out, the variables that are very significant in determining the price of cars include the fuel type (gas HP), car body(sedan HP),engine location(front HP), engine size(100 HP), horsepower(horsepower HP), aspiration(std, HP) and drive wheel (fwd HP). HP = Highest price

NOTABLE OBSERVATIONS FROM THE RESEARCH

1. The most purchase cars are those of Toyota corona and Toyota corolla series (sorted 6 times more), followed by the Toyota mark ii (3 times more than others.
2. Cars with the highest price are mostly cars from the volvo company.
3. The fuel type gas is by far preferred than the diesel type likewise the standard cars to diesel cars.
4. There is no significant difference between the two and four door cars as to preference.
5. Cars with sedan body type and those with fore wheel drive are mostly preferred.
6. Preference to engine size is those with ones between 100 and 150 and to horsepower, those between 50 to 150 as observed.
7. The more the price of the cars the lower the number of purchase with the highest purchase at price 5000 and price range 5000 to 20000. Cars out of this range are generally less purchase though at price 35000 slightly more sales is observed.
8. Cars with sedan bodies are consistently preferred even with increase in price.
9. There is increase in price with the increase in horsepower.

RECOMMENDATIONS

1. Cars with sedan car body, gas fuel type, engine size between 100 and 150, horse power of 50 to 150 with prices ranging from 5000 to 2000 are highly recommended for the best sales in Nigeria.
2. The company should consider producing cars with hardtop body type with more catchy features such as ones captured in 1 above as this car type is price highest when car body type is in consideration.
3. A new model of car with engine in the front (away from the conventional) with the features as in 1 and 2 above should be produce, this will have the company positioned at an edge that almost no one has explore and still leaving the company with high sales of such a car and gross profit profit at the end.